Scoping Review of Machine Learning and Deep Learning Algorithm Applications in Veterinary Clinics: Situation Analysis and Suggestions for Further Studies

Abstract

Machine learning and deep learning (ML/DL) algorithms have been successfully applied in medical practice.However, their application in veterinary medicine is relatively limited, possibly due to a lack in the quantity and quality of relevant research.Because the potential demands for ML/DL applications in veterinary clinics are significant, it is important to note the current gaps in the literature and explore the possible directions for advancement in this field.Thus, a scoping review was conducted as a situation analysis.We developed a search strategy following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.PubMed and Embase databases were used in the initial search.The identified items were screened based on predefined inclusion and exclusion criteria.Information regarding model development, quality of validation, and model performance was extracted from the included studies.The current review found 55 studies that passed the criteria.In terms of target animals, the number of studies on industrial animals was similar to that on companion animals.Quantitative scarcity of prediction studies (n = 11, including duplications) was revealed in both industrial and non-industrial animal studies compared to diagnostic studies (n = 45, including duplications).Qualitative limitations were also identified, especially regarding validation methodologies.Considering these gaps in the literature, future studies examining the prediction and validation processes, which employ a prospective and multi-center approach, are highly recommended.Veterinary practitioners should acknowledge the current limitations in this field and adopt a receptive and critical attitude towards these new technologies to avoid their abuse.

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